Artificial Neural Network Modeling Applied for Predicting Reformate Yield and Research Octane Number in the Reforming Process

Authors

Badiea S. Babaqi, Department of Chemical Engineering, Faculty of Engineering and Petroleum, Hadhramout University, Mukalla, Hadhramout, YemenFollow
Abdelrigeeb Ali Al-Gathe, Department of Petroleum Engineering, Faculty of Engineering and Petroleum, Hadhramout University, Mukalla, Hadhramout, Yemen
Mohd S. Takriff, Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Hassimi Abu Hasan, Research Centre for Sustainable Process Technology, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia.
Mohammed H. Al-Douh, Department of Chemistry, Faculty of Science, Hadhramout University, Mukalla, Hadhramout, Yemen.

Abstract

The prediction model of the continuous catalytic regeneration reforming process was developed for expecting the reformate yield and research octane number using an Artificial Neural Network technique (ANN) to improve the process performance. The proposed model includes temperatures, pressures, and hydrogen to hydrocarbon molar ratio as input parameters while the output of the process represents reformate yield and research octane number. The ANN model was carried out to estimate the process behavior based on the Levenberg-Marquardt Algorithm, which included the nine input parameters, two hidden layers (10-5 neurons), and two parameters as network outputs. The results obtained were that the prediction error for the reformate test was 0.0027 with a regression of 0.9995, while the research octane number was 0.0026 with a regression of 0.9979. The proposed model showed the ability of Artificial Intelligence to predict either the yield & octane number or simulate the behavior of the process with more accurate.

Recommended Citation

Babaqi, Badiea S.; Al-Gathe, Abdelrigeeb Ali; Takriff, Mohd S.; Abu Hasan, Hassimi; and Al-Douh, Mohammed H. (2023) "Artificial Neural Network Modeling Applied for Predicting Reformate Yield and Research Octane Number in the Reforming Process," Hadhramout University Journal of Natural & Applied Sciences: Vol. 20: Iss. 2, Article 1.
Available at: https://digitalcommons.aaru.edu.jo/huj_nas/vol20/iss2/1